diff --git a/topical-units/env-r/README.md b/topical-units/env-r/README.md
index 9db6442daec620f6868a1ad9c9cd43b32dedfdd2..42be5f17ae117b3c8f9c068c7c65dc4db4f658ea 100644
--- a/topical-units/env-r/README.md
+++ b/topical-units/env-r/README.md
@@ -3,8 +3,20 @@
 R is a programming language widely used by domain scientists (i.e. biologists, physicists) to visualize datasets. The focus of this unit will be on the language, but it will also cover the R terminal console and RStudio, as well as the common R package ggplot. This unit will not cover deeper nuances of data science, statistics, and modeling, except incidentally.
 
 # Resources
+ - [Rstudio Web (rstudio.cluster.earlham.edu)](https://rstudio.cluster.earlham.edu/)
  - [Install R and R Studio](https://posit.co/download/rstudio-desktop/)
  - [Intro to ggplot2](https://towardsdatascience.com/r-for-beginners-learn-how-to-visualize-data-like-a-pro-840d1828c09c)
+ - [Software Carpentry R Tutorial](https://swcarpentry.github.io/r-novice-gapminder/)
+
+## Getting Started 
+You have a few different options for using R:
+1. The lab computers in Lovelace (CST 219).
+2. Installing R Studio on your own person machine (Link in resources).
+3. Use Rstudio web on ECCS servers (Link in resources).
+
+Once you have an Rstudio session with any of these options, take some time to explore and learn about the interface. Test out some of the basic instructions from any of the resources above, and ask questions when you have them.
+
+When you feel comfortable navigating, proceed to the deliverables below.
 
 # Deliverables
 
@@ -19,5 +31,3 @@ Here are the data analyses and visualizations we need to see by end of term:
 * Create four (different) R visualizations comparing a few categories within a dataset (could be a line graph, bar graph, box-and-whisker, or another visualization that fits the data).
 * For each visualization, write a caption.
 * Make your visualizations look good! (Really, this will be part of your evaluation.)
-
-Two servers, [bowie](https://jupyter.cs.earlham.edu) and [bronte](https://bronte.cluster.earlham.edu), have notebook environments for R. They should have all relevant libraries, but if we've missed one we can install it quickly.